In this last installment, we are diving into predictive analytics - a hot topic in our industry, and for good reason: powered by machine-learning and leveraging existing data, no other scientific area of study has such vast application possibilities in the areas of customer service, loyalty, and satisfaction. Consider that Big Data is still a challenge, add to that, the limited bandwidth of most contact center managers - you need time, lots of it, to extract, classify, format, and generate information from said unstructured data.
But that data holds information that is invaluable for evaluating and improving operations. especially the human part of your operations: contact center agents - our most valuable and by the same measure, most expensive asset - especially when we squander their potential.
Let us begin with a baseline definition of predictive analytics, compliments of Gartner Research, and cover how we leverage our research, experience and contact center data within our customer base.
Predictive analytics describes any approach to data mining with four attributes:
1. An emphasis on prediction, rather than description, classification or clustering.
2. Rapid analysis measured in hours or days, rather than the typical months of traditional data mining.
3. An emphasis on the business relevance of the resulting insights.
4. An emphasis on ease of use, thus making the tools accessible to business users.
To sum up points 1 through 4, data must be collected, but rather than pipe that data into typical reports, we leverage it to provide forecasting, or predictions, for business outcomes.
And just as importantly, we accomplish this without the need for technical expertise from the customer. What good is it to supply such a sophisticated application, and simultaneously ask our clients to add a programmer-analyst to their contact center team (or if they have one, add to their workload). The point of predictive analytics is to save time and increase productivity!
Underlying our approach to predictive analytics is a sophisticated Machine Learning model encompassing several contact center business-specific real-world scenarios.
Those scenarios have been universally validated by our potential and existing customers, as well as the professional community: agent attrition is at the top, and vies with schedule adherence and other baseline performance metrics as the top concerns for contact center leaders. Guess what the third concern happens to be? That's right: inaccurate data reporting and analysis.
In the depiction below, we are specifically elaborating on the agent side of the equation, and putting aside reporting for the moment.
Although we covered employee engagement in a previous article, it's important to mention that without the deep learning and data analysis components of our product, there would be no behavioral patterns available for us upon which to create increasingly challenging gaming elements, or provide the continuous engagement model that benefits an agent's on-the-job performance throughout their entire career lifecycle.
Let's add reporting and data analysis back into the equation.
Sales projections are a notoriously contentious area - debates range from past performance as a benchmark to social and macro-economic influencers.
We use only what is out of contention as data inputs: certainly, past performance, since it provides cyclical context (seasonality, duration of buying cycle), but additionally, we leverage everything logged in your contact center system of choice: ACD, IVR, you name it. Add the agents peak performance periods into the mix, and you have something much more robust upon which to build your pipeline intelligence.
Now that we have covered our 3 pillars, let's look at one of our premier partner's pillars for the same period.
We hope you have benefited from our roadmap reveal, and look forward to more of your input. If you would like more information on how we can transform your contact center, post your comments, or get in touch with us on our demo page.
Have a successful and profitable 2017!